Awesome

Table of Contents

• DREAMPlaceFPGA
  • FPGA Placement: Overview
    • Global Placer
    • Packer-Legalizer
  • Timing-driven Placement
  • Performance
  • Target Architecture
• Publications
• Developers
• External Dependencies
• Cloning the Repository
• Build Instructions
  • To install Python dependency
  • To install with Docker
  • To Build
  • Cmake Options
• Sample Benchmarks
• Running DREAMPlaceFPGA
• JSON Configurations
• Bug Report
• Copyright

<a name="dreamplacefpga"></a>DREAMPlaceFPGA

DREAMPlaceFPGA is an Open-Source GPU-Accelerated Placer for Large Scale Heterogeneous FPGAs using a Deep Learning Toolkit.

<a name="fpga_placement"></a>FPGA Placement: An Overview

Placement is a crucial and computationally intensive step in the FPGA design flow that determines the physical locations of various heterogeneous instances in the design. In general, placement consists of three stages - global placement (GP), packing/clustering and legalization (LG), and detailed placement (DP).

• Stages in an FPGA Placement

With a synthesized logic-mapped design netlist and the FPGA architecture description as inputs, the global placer obtains roughly legal locations for all the design instances. Based on the global placement (GP) solution, the packer clusters the FFs and LUTs to be placed on the corresponding sites. Then, the legalizer assigns all the instances to their corresponding physical sites on the FPGA to obtain a legal placement. A placement is legal when an instance occupies a site of the same type, and all instances can be routed with the limited routed resources available on an FPGA. The disruptive movement of instances to the fixed physical site locations during legalization results in quality degradation, and thus, detailed placement (DP) further refines the legalized placement.

Among the various placement stages, the global placement and pack-legalize stages are accelerated in DREAMPlaceFPGA.

<a name="global_placer"></a>Global Placer

By leveraging the open-source ASIC placement framework, DREAMPlace, we built an open-source placement framework for FPGAs that is based on the elfPlace algorithm. The global placement flow in DREAMPlaceFPGA:

<a name="packer_legalizer"></a>Packer-Legalizer

Starting with a flat global placement solution, the direct legalization (DL) algorithm allows for clustering (or) packing of LUTs and FFs, followed by legalization to their respective sites. The packing-legalization flow in DREAMPlaceFPGA:

DREAMPlaceFPGA enhances the DL algorithm and accelerates it on a GPU.

<a name="timing_driven_placement"></a>Timing-driven Placement

Timing awareness is incorporated in the global placer and packer-legalizer as illustrated:

The timing-driven placer constructs a timing graph for all timing paths as source-load pairs from the design, architecture, and timing model information. In addition to reducing the overall wirelength and overlaps, the global placer optimizes the timing paths once the instance overlaps are minimal.

During a timing GP iteration, the slacks for all the timing arcs are computed based on the current instance locations. Timing criticality is also considered during LUT/FF packing and legalization through cluster scoring functions.

The timing arc wirelength and timing preconditioner operators are accelerated on GPU.

<a name="performance"></a>Performance

DREAMPlaceFPGA outperforms elfPlace (GPU) by 19% for global placementruntime. On the ISPD'2016 benchmark suite, DREAMPlaceFPGA is 5.3× faster for global placement, 2.2× faster for packing-legalization and 2.4× faster for overall placement than 16-thread elfPlace (CPU), with a slight increase in (+0.6%) placement HPWL and (+0.9%) routed wirelength.

The timing-driven version (GPU) achieves a 2% higher post-route geomean critical path delay (CPD), a 3x faster placement runtime than Vivado 2022.1, and a reduction of Vivado routing runtime by 40%. Please refer to the 'publications' for more details.

The results vary depending on the hardware used. The above-mentioned results are based on a Linux machine with an Intel i9-7900 CPU (running at 3.30 GHz) and an NVIDIA Titan Xp (Pascal) GPU.

<a name="target_arch"></a>Target Architecture

Support for the following architectures is available

Simplified Xilinx Ultrascale Architecture

• The ISPD'2016 benchmarks, which employs a simplified Xilinx Ultrascale architecture, can be run on DREAMPlaceFPGA.
• The lightweight data-driven timing model, consisting of logic and net delay values, is available. Please refer to timing_README for details on running timing-driven placement.
• Interchange Format (IF) support is not available for this architecture.

Ultrascale+ Architecture

• Limited support is available for Ultrascale+ architecture GNL designs through the Interchange Format (IF). For more details, please refer to IF_README.

The elfPlace (CPU) binary is available to run the legalization and detailed placement stages, when DREAMPlaceFPGA is used to run only the global placement stage. DREAMPlaceFPGA runs on both CPU and GPU. If installed on a machine without GPU, multi-threaded CPU support is available.

<a name="publications"></a>Publications

• Zhili Xiong, Rachel Selina Rajarathnam, and David Z. Pan, "A Data-Driven, Congestion-Aware and Open-Source Timing-Driven FPGA Placer Accelerated by GPUs," The 32nd IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM), 2024. (accepted)
• Rachel Selina Rajarathnam, Zixuan Jiang, Mahesh A. Iyer, and David Z. Pan, "DREAMPlaceFPGA-PL: An Open-Source GPU-Accelerated Packer-Legalizer for Heterogeneous FPGAs", ACM International Symposium on Physical Design (ISPD), 2023.
• Rachel Selina Rajarathnam, Mohamed Baker Alawieh, Zixuan Jiang, Mahesh A. Iyer, and David Z. Pan, "DREAMPlaceFPGA: An Open-Source Analytical Placer for Large Scale Heterogeneous FPGAs using Deep-Learning Toolkit", 27th IEEE/ACM Asian and South Pacific Design Automation Conference (ASP-DAC), 2022.

<a name="developers"></a>Developers

• Rachel Selina Rajarathnam, UTDA, ECE Department, The University of Texas at Austin
• Zhili Xiong, UTDA, ECE Department, The University of Texas at Austin
• Zhixing (Ethan) Jiang, UTDA, ECE Department, The University of Texas at Austin
• Zixuan Jiang, UTDA, ECE Department, The University of Texas at Austin

<a name="dependencies"></a>External Dependencies

• Python 2.7 or Python 3.5/3.6/3.7

• CMake version 3.8.2 or later

• Pytorch 1.6/1.7/1.8

  • Other 1.0 versions may also work, but not tested.
  • Version 2.0 or higher is not compatible!

• GCC

  • Recommend GCC 5.1 or later.
  • Other compilers may also work but have not been tested.

• cmdline

  • a command line parser for C++

• Flex

  • lexical analyzer employed in the bookshelf parser

• Bison

  • parser generator employed in the bookshelf parser

• Boost

  • Need to install and be visible for linking

• Limbo

  • Integrated as a submodule: the bookshelf parser is modified for FPGAs.

• Flute

  • Integrated as a submodule

• CUB

  • Integrated as a git submodule

• munkres-cpp

  • Integrated as a git submodule

• CUDA 9.1 or later (Optional)

  • If installed and found, GPU acceleration will be enabled.
  • Otherwise, only CPU implementation is enabled.
  • Version 12.0 or higher is not compatible!

• GPU architecture compatibility 6.0 or later (Optional)

  • Code has been tested on GPUs with compute compatibility 6.0, 7.0, 7.5, and 8.0. <a name="compute_capability"></a>
  • Please check the compatibility of the GPU devices.
  • The default compilation target is compatibility 6.0. This is the minimum requirement, and lower compatibility is not supported for the GPU feature.

• Cairo (Optional)

  • If installed and found, the plotting functions will be faster by using C/C++ implementation.
  • Otherwise, python implementation is used.

• pycapnp

  • Python bindings are required for Interchange Format (IF) support.

<a name="cloning"></a>Cloning the Repository

To pull git submodules in the root directory

git submodule init
git submodule update

Alternatively, pull all the submodules when cloning the repository.

git clone --recursive https://github.com/rachelselinar/DREAMPlaceFPGA.git

<a name="build"></a>Build Instructions

<a name="python_dependency"></a>To install Python dependency

There is an alternative way to install DREAMPlaceFPGA using Docker; if you want to use Docker, skip this step and proceed to the next step, "To install with Docker".

At the root directory:

pip install -r requirements.txt 

For example, if the repository was cloned in directory ~/Downloads, then the root directory is ~/Downloads/DREAMPlaceFPGA

You can also use a python virtual environment to install all the required packages to run DREAMPlaceFPGA

<a name="Docker"></a>To install with Docker

You can use the Docker container to avoid building all the dependencies yourself.

1. Install Docker on Linux. (Win and Mac are not tested)

2. To enable the GPU features, install NVIDIA-docker; otherwise, skip this step.

3. Get the docker image using one of the options Build the image locally

   docker build . --file Dockerfile --tag <username>/dreamplacefpga:1.0
   

   Replace <username> with a username, for instance, 'utda_placer.'

4. Enter the bash environment of the container. Mount the repo and all the Designs into the Docker, which allows the Docker container to access and modify these files directly.

   To run on a Linux machine without GPU:

   docker run -it -v $(pwd):/DREAMPlaceFPGA <username>/dreamplacefpga:1.0 bash
   

   To run on a Linux machine with GPU: (Docker verified on NVIDIA GPUs with compute capability 6.1, 7.5, and 8.0)

   docker run --gpus 1 -it -v $(pwd):/DREAMPlaceFPGA <username>/dreamplacefpga:1.0 bash
   

   For example, to run on a Linux machine without GPU:

   docker run -it -v $(pwd):/DREAMPlaceFPGA utda_placer/dreamplacefpga:1.0 bash
   

5. Go to the DREAMPlaceFPGA directory in Docker, which is the root directory of the project

   cd /DREAMPlaceFPGA
   

<a name="build_dreamplacefpga"></a>To Build

At the root directory,

mkdir build 
cd build 
cmake .. -DCMAKE_INSTALL_PREFIX=path_to_root_dir
make
make install

If you are using the docker, use these commands at the root directory,

rm -rf build
mkdir build 
cd build 
cmake .. -DCMAKE_INSTALL_PREFIX=/DREAMPlaceFPGA -DPYTHON_EXECUTABLE=$(which python)
make
make install

Third-party submodules are automatically built except for Boost.

For example,

~/Downloads/DREAMPlaceFPGA: mkdir build; cd build

~/Downloads/DREAMPlaceFPGA/build: cmake . . -DCMAKE_INSTALL_PREFIX=~/Downloads/DREAMPlaceFPGA

~/Downloads/DREAMPlaceFPGA/build: make; make install

The directory ~/Downloads/DREAMPlaceFPGA/build is the install dir

When packages or parser code are changed, the contents of the build directory must be deleted for a clean build and proper operation.

rm -r build

For example,

~/Downloads/DREAMPlaceFPGA: rm -r build

<a name="cmake"></a>Cmake Options

Here are the available options for CMake.

• CMAKE_INSTALL_PREFIX: installation or root directory
  • Example cmake -DCMAKE_INSTALL_PREFIX=path/to/root/directory
• CMAKE_CUDA_FLAGS: custom string for NVCC (default -gencode=arch=compute_60,code=sm_60)
  • Each GPU has a specific compute capability. Use this flag if your GPU's compute capability is not included in the default settings
  • Example for compute capability of 6.0: cmake -DCMAKE_CUDA_FLAGS=-gencode=arch=compute_60,code=sm_60
• CMAKE_CXX_ABI: 0|1 for the value of _GLIBCXX_USE_CXX11_ABI for C++ compiler, default is 0.
  • Example cmake -DCMAKE_CXX_ABI=0
  • It must be consistent with the _GLIBCXX_USE_CXX11_ABI for compiling all the C++ dependencies, such as Boost and PyTorch.
  • PyTorch is compiled with _GLIBCXX_USE_CXX11_ABI=0 by default, but in a customized environment, it might be compiled with _GLIBCXX_USE_CXX11_ABI=1.

<a name="sample"></a>Sample Benchmarks

DREAMPlaceFPGA requires IO instances to be fixed and only accepts inputs in Bookshelf format.

• 5 sample benchmarks for Xilinx Ultrascale Architecture in bookshelf format with fixed IOs are provided. To run timing-driven placement, please refer to timing_README.
• 10 GNL designs based on Xilinx Ultrascale+ Architecture are provided. These benchmarks must be converted from Interchange Format (IF) to bookshelf format before running DREAMPlaceFPGA. Refer to IF_README for more details.

The sample benchmarks and designs are in the benchmarks directory.

<a name="running"></a>Running DREAMPlaceFPGA

Before running, ensure that all python dependent packages have been installed. Go to the root directory and run with the JSON configuration file.

python dreamplacefpga/Placer.py <benchmark>.json

Run from ~/Downloads/DREAMPlaceFPGA directory

For example:

python dreamplacefpga/Placer.py test/FPGA-example1.json

~/Downloads/DREAMPlaceFPGA: python dreamplacefpga/Placer.py test/FPGA-example1.json

If you are not using the GPU, change the gpu flag in the *.json file to 0.

Unit tests for some of the pytorch operators are provided. For instance, to run the unit test for hpwl, use the below command:

python unitest/ops/hpwl_unitest.py

Note: If your machine does not have an NVIDIA GPU, set the 'gpu' flag in the JSON configuration file to '0' to run on the CPU.

<a name="json"></a>JSON Configurations

The most frequently used options in the JSON file are listed below. For the complete list of available options, please refer to paramsFPGA.json.

<a name="bug"></a>Bug Report

To report a bug, please file an issue.

<a name="copyright"></a>Copyright

This software is released under a BSD 3-Clause "New" or "Revised" License. Please refer to LICENSE for details.